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This course focuses on topics related to reinforcement learning. The course will cover making decisions under uncertainty, Markov decision processes, dynamic programming, temporal-difference learning, eligibility traces, value function approximation methods, Monte Carlo reinforcement learning methods, and the integration of learning and planning.
Weekly Contact: Lecture: 3 hrs.
GPA Weight: 1.00
Course Count: 1.00
Billing Units: 1
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*List may not include courses that are on a common table shared between programs.